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  2. Gaussian elimination - Wikipedia

    en.wikipedia.org/wiki/Gaussian_elimination

    A variant of Gaussian elimination called Gauss–Jordan elimination can be used for finding the inverse of a matrix, if it exists. If A is an n × n square matrix, then one can use row reduction to compute its inverse matrix, if it exists. First, the n × n identity matrix is augmented to the right of A, forming an n × 2n block matrix [A | I]

  3. Row echelon form - Wikipedia

    en.wikipedia.org/wiki/Row_echelon_form

    A matrix is in reduced row echelon form if it is in row echelon form, with the additional property that the first nonzero entry of each row is equal to and is the only nonzero entry of its column. The reduced row echelon form of a matrix is unique and does not depend on the sequence of elementary row operations used to obtain it.

  4. Reduction (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Reduction_(mathematics)

    In the case of matrices, the process involves manipulating either the rows or the columns of the matrix and so is usually referred to as row-reduction or column-reduction, respectively. Often the aim of reduction is to transform a matrix into its "row-reduced echelon form" or "row-echelon form"; this is the goal of Gaussian elimination.

  5. Pivot element - Wikipedia

    en.wikipedia.org/wiki/Pivot_element

    A pivot position in a matrix, A, is a position in the matrix that corresponds to a row–leading 1 in the reduced row echelon form of A. Since the reduced row echelon form of A is unique, the pivot positions are uniquely determined and do not depend on whether or not row interchanges are performed in the reduction process. Also, the pivot of a ...

  6. Row and column spaces - Wikipedia

    en.wikipedia.org/wiki/Row_and_column_spaces

    This makes it possible to use row reduction to find a basis for the row space. For example, consider the matrix = []. The rows of this matrix span the row space, but they may not be linearly independent, in which case the rows will not be a basis. To find a basis, we reduce A to row echelon form: r 1, r 2, r 3 represents the rows.

  7. Rank factorization - Wikipedia

    en.wikipedia.org/wiki/Rank_factorization

    In practice, we can construct one specific rank factorization as follows: we can compute , the reduced row echelon form of .Then is obtained by removing from all non-pivot columns (which can be determined by looking for columns in which do not contain a pivot), and is obtained by eliminating any all-zero rows of .

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  9. Row equivalence - Wikipedia

    en.wikipedia.org/wiki/Row_equivalence

    In particular, any two row equivalent matrices have the same row space. Any matrix can be reduced by elementary row operations to a matrix in reduced row echelon form. Two matrices in reduced row echelon form have the same row space if and only if they are equal. This line of reasoning also proves that every matrix is row equivalent to a unique ...